I-Corps: Accurate GPS-free Navigation and Localization

I-Corps项目的更广泛影响/商业潜力是开发自主导航技术，使系统能够在没有全球定位系统的不确定环境中稳健运行。该项目是天文学、航空航天、计算科学和人工智能融合的结果。这项技术的商业化有可能彻底改变太空探索、自动驾驶汽车、无人机（UAV）和其他需要精确位置估计的系统。该项目技术的一个关键优势是增强了网络安全性，因为它不依赖外部信号进行导航。此外，该项目将为科学界提供开放源码软件。据设想，开发一个软件工具箱，集成了流行的ROS（机器人操作系统）库将允许研究人员模拟自主导航没有GPS。这个I-Corps项目是研究的结果，同时定位和地图（SLAM）的问题。在SLAM中，机器人没有获得其环境的先验知识，它必须使用其传感数据和动作来同时构建其环境的地图并在其不确定的地图中定位自己。在这一领域的竞争方法表现出定位误差，这可能是不适合长期导航。这里开发的工作表明，通过融合方向感测与短程感测，系统实现了基本优化问题的简化。这允许快速和全局最优的解决方案。在这种方法中，车辆使用相机来跟踪天空中的天体，这允许车辆估计其在空间中的方位，该信息与短距离传感器（例如，跟踪车辆附近的特征的激光器和相机）融合。 使用所提出的方法，系统可以实现100倍的改进，在现有的方法的位置误差。

The broader impact/commercial potential of this I-Corps project is to develop autonomous navigation technology that will enable systems to robustly operate in uncertain environments without a Global Positioning System (GPS). The project is a result of a confluence of astronomy, aerospace, computational science and artificial intelligence. Commercialization of this technology has the potential to revolutionize space exploration, self-driving cars, Unmanned Aerial Vehicles (UAVs) and other such systems which need accurate position estimation. A key advantage of this project's technology is enhanced cybersecurity as it does not rely on external signals for navigation. Further, this project will contribute open-source software to the scientific community. It is envisioned that development of a software toolbox that integrates with the popular ROS (Robot Operating System) library will allow researchers to simulate autonomous navigation without GPS.This I-Corps project is a result of research into the problem of Simultaneous Localization and Mapping (SLAM). In SLAM, a robot is not given prior knowledge of its environment, it must use its sensory data and actions to simultaneously build a map of its environment and position itself within its uncertain map. Competing methods in this area exhibit positioning errors which may be unsuitable for long-term navigation. The work developed here shows that by fusing orientation sensing with short-range sensing, the system attain a simplification of the underlying optimization problem. This allows fast and globally optimal solutions. In this approach, a vehicle uses a camera to track celestial bodies in the sky which allows the vehicle to estimate its orientation in space, this information is fused with short-range sensors such as lasers and cameras which track features in vicinity of the vehicle. Using the proposed approach, a system can achieve 100x improvement in position error over existing methods.